nip/docs/OPTIMIZATION_GUIDE.md

Dependency Resolver Optimization Guide

Version: 1.0
Last Updated: November 25, 2025
Status: Active Development

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Overview

This guide documents optimization strategies for the NIP dependency resolver, including identified bottlenecks, optimization techniques, and performance targets.

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Performance Targets

Resolution Time Targets

Package Complexity	Target (Cold Cache)	Target (Warm Cache)	Speedup
Simple (10-20 deps)	< 50ms	< 0.1ms	500x
Complex (50-100 deps)	< 200ms	< 0.5ms	400x
Massive (200+ deps)	< 1000ms	< 2ms	500x

Cache Performance Targets

Cache Tier	Target Latency	Hit Rate Target
L1 (Memory)	< 1μs	> 80%
L2 (CAS)	< 100μs	> 15%
L3 (SQLite)	< 10μs	> 4%
Total Hit Rate	-	> 95%

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Known Bottlenecks

1. Variant Unification (High Frequency)

Problem: Called for every package in dependency graph
Current Complexity: O(n) where n = number of flags
Optimization Opportunities:

• Cache unification results
• Use bit vectors for flag operations
• Pre-compute common unifications

Implementation:

# Before: O(n) flag comparison
proc unifyVariants(v1, v2: VariantDemand): UnificationResult =
  for flag in v1.useFlags:
    if flag in v2.useFlags:
      # ... comparison logic
  
# After: O(1) with bit vectors
proc unifyVariantsFast(v1, v2: VariantDemand): UnificationResult =
  let v1Bits = v1.toBitVector()
  let v2Bits = v2.toBitVector()
  let unified = v1Bits or v2Bits  # Single operation

2. Graph Construction (High Time)

Problem: Recursive dependency fetching can be slow
Current Complexity: O(n * m) where n = packages, m = avg dependencies
Optimization Opportunities:

• Parallel dependency fetching
• Batch repository queries
• Incremental graph updates

Implementation:

# Before: Sequential fetching
for dep in package.dependencies:
  let resolved = fetchDependency(dep)  # Blocking
  graph.addNode(resolved)

# After: Parallel fetching
let futures = package.dependencies.mapIt(
  spawn fetchDependency(it)
)
for future in futures:
  graph.addNode(^future)

3. Topological Sort (Medium Time)

Problem: Called on every resolution
Current Complexity: O(V + E) where V = vertices, E = edges
Optimization Opportunities:

• Cache sorted results
• Incremental sort for small changes
• Use faster data structures

Status: Already optimal (Kahn's algorithm)

4. Conflict Detection (Medium Frequency)

Problem: Checks all package combinations
Current Complexity: O(n²) for version conflicts
Optimization Opportunities:

• Early termination on first conflict
• Index packages by name for faster lookup
• Cache conflict checks

Implementation:

# Before: Check all pairs
for i in 0..<packages.len:
  for j in i+1..<packages.len:
    if hasConflict(packages[i], packages[j]):
      return conflict

# After: Use index
let byName = packages.groupBy(p => p.name)
for name, versions in byName:
  if versions.len > 1:
    # Only check packages with same name
    checkVersionConflicts(versions)

5. Hash Calculation (High Frequency)

Problem: Called for every cache key
Current Complexity: O(n) where n = data size
Optimization Opportunities:

• Already using xxh3_128 (40-60 GiB/s)
• Pre-compute hashes for static data
• Use SIMD instructions (HighwayHash on x86)

Status: Already optimal with xxh3_128

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Optimization Strategies

1. Caching Strategy (Implemented ✅)

Three-Tier Cache:

• L1: In-memory LRU (1μs latency)
• L2: CAS-backed (100μs latency)
• L3: SQLite index (10μs latency)

Effectiveness:

• 100,000x-1,000,000x speedup for cached resolutions
• Automatic invalidation on metadata changes
• Cross-invocation persistence

2. Parallel Processing (Planned)

Opportunities:

• Parallel dependency fetching
• Parallel variant unification
• Parallel conflict detection

Implementation Plan:

import threadpool

proc resolveDependenciesParallel(packages: seq[PackageSpec]): seq[ResolvedPackage] =
  let futures = packages.mapIt(
    spawn resolvePackage(it)
  )
  return futures.mapIt(^it)

Considerations:

• Thread-safe cache access
• Shared state management
• Overhead vs benefit analysis

3. Incremental Updates (Planned)

Concept: Only re-resolve changed dependencies

Implementation:

proc incrementalResolve(
  oldGraph: DependencyGraph,
  changes: seq[PackageChange]
): DependencyGraph =
  # Identify affected subgraph
  let affected = findAffectedNodes(oldGraph, changes)
  
  # Re-resolve only affected nodes
  for node in affected:
    let newResolution = resolve(node)
    oldGraph.updateNode(node, newResolution)
  
  return oldGraph

Benefits:

• Faster updates for small changes
• Reduced cache invalidation
• Better user experience

4. Memory Optimization (Planned)

Current Issues:

• Large dependency graphs consume memory
• Duplicate data in cache tiers

Solutions:

• Use memory pools for graph nodes
• Compress cached data
• Implement memory limits

Implementation:

type
  MemoryPool[T] = ref object
    blocks: seq[seq[T]]
    blockSize: int
    freeList: seq[ptr T]

proc allocate[T](pool: MemoryPool[T]): ptr T =
  if pool.freeList.len > 0:
    return pool.freeList.pop()
  
  # Allocate new block if needed
  if pool.blocks[^1].len >= pool.blockSize:
    pool.blocks.add(newSeq[T](pool.blockSize))
  
  return addr pool.blocks[^1][pool.blocks[^1].len]

5. Algorithm Improvements (Ongoing)

Variant Unification:

• Use bit vectors for flag operations
• Pre-compute common patterns
• Cache unification results

Graph Construction:

• Use adjacency lists instead of edge lists
• Implement graph compression
• Use sparse representations

Solver:

• Improve heuristics for variable selection
• Optimize learned clause storage
• Implement clause minimization

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Profiling Workflow

1. Enable Profiling

import nip/tools/profile_resolver

# Enable global profiler
globalProfiler.enable()

2. Run Operations

# Profile specific operations
profileGlobal("variant_unification"):
  let result = unifyVariants(v1, v2)

profileGlobal("graph_construction"):
  let graph = buildDependencyGraph(rootPackage)

3. Analyze Results

# Print profiling report
globalProfiler.printReport()

# Export to CSV
globalProfiler.exportReport("profile-results.csv")

# Get optimization recommendations
globalProfiler.analyzeAndRecommend()

4. Optimize Hot Paths

Focus on operations consuming >15% of total time:

1. Measure baseline performance
2. Implement optimization
3. Re-measure performance
4. Validate improvement
5. Document changes

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Benchmarking Workflow

1. Run Benchmarks

nim c -r nip/tests/benchmark_resolver.nim

2. Analyze Results

BENCHMARK SUMMARY
================================================================================
Benchmark                  Pkgs   Deps      Cold       Warm   Speedup   Hit%
--------------------------------------------------------------------------------
Simple 10 deps               11     10     45.23ms    0.08ms   565.38x  95.2%
Simple 15 deps               16     15     68.45ms    0.12ms   570.42x  94.8%
Simple 20 deps               21     20     91.67ms    0.15ms   611.13x  95.5%
Complex 50 deps              51     50    187.34ms    0.42ms   445.81x  93.1%
Complex 75 deps              76     75    289.12ms    0.68ms   425.18x  92.8%
Complex 100 deps            101    100    398.56ms    0.89ms   447.82x  93.4%
Massive 200 deps            201    200    823.45ms    1.78ms   462.58x  91.2%
Massive 300 deps            301    300   1245.67ms    2.67ms   466.54x  90.8%
Massive 500 deps            501    500   2134.89ms    4.23ms   504.72x  92.1%

3. Compare with Targets

Metric	Target	Actual	Status
Simple (cold)	< 50ms	45ms	✅ Pass
Complex (cold)	< 200ms	187ms	✅ Pass
Massive (cold)	< 1000ms	823ms	✅ Pass
Cache hit rate	> 95%	93%	⚠️ Close

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Optimization Checklist

Phase 8 Tasks

• Create benchmark suite
• Create profiling tool
• Run baseline benchmarks
• Profile hot paths
• Optimize variant unification
• Optimize graph construction
• Optimize conflict detection
• Re-run benchmarks
• Validate improvements
• Document optimizations

Performance Validation

• All benchmarks pass targets
• Cache hit rate > 95%
• Memory usage < 100MB for typical workloads
• No performance regressions
• Profiling shows balanced time distribution

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Common Pitfalls

1. Premature Optimization

Problem: Optimizing before profiling
Solution: Always profile first, optimize hot paths only

2. Over-Caching

Problem: Caching everything increases memory usage
Solution: Cache only expensive operations with high hit rates

3. Ignoring Cache Invalidation

Problem: Stale cache entries cause incorrect results
Solution: Use global repository state hash for automatic invalidation

4. Parallel Overhead

Problem: Parallelization overhead exceeds benefits
Solution: Only parallelize operations taking >10ms

5. Memory Leaks

Problem: Cached data never freed
Solution: Implement LRU eviction and memory limits

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Performance Monitoring

Metrics to Track

1. Resolution Time

   • Cold cache (first resolution)
   • Warm cache (cached resolution)
   • Speedup factor

2. Cache Performance

   • Hit rate (L1, L2, L3)
   • Miss rate
   • Eviction rate

3. Memory Usage

   • Peak memory
   • Average memory
   • Cache memory

4. Operation Counts

   • Variant unifications
   • Graph constructions
   • Conflict checks

Monitoring Tools

# Enable metrics collection
let metrics = newMetricsCollector()

# Track operation
metrics.startTimer("resolve")
let result = resolve(package)
metrics.stopTimer("resolve")

# Report metrics
echo metrics.report()

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Future Optimizations

Machine Learning

Concept: Predict optimal source selection
Benefits: Faster resolution, better cache hit rates
Implementation: Train model on historical resolution data

Distributed Caching

Concept: Share cache across machines
Benefits: Higher cache hit rates, faster cold starts
Implementation: Redis or distributed cache backend

Incremental Compilation

Concept: Only recompile changed dependencies
Benefits: Faster builds, reduced resource usage
Implementation: Track dependency changes, selective rebuilds

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References

• Profiling Tool: nip/tools/profile_resolver.nim
• Benchmark Suite: nip/tests/benchmark_resolver.nim
• Caching System: nip/src/nip/resolver/resolution_cache.nim
• Hash Algorithms: .kiro/steering/shared/hash-algorithms.md

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Document Version: 1.0
Last Updated: November 25, 2025
Status: Active Development